Thermo- and photo-reversible reactions for the preparation of smart materials : smart rubbers and recyclable shape memory polymers

audience: researche

Dutch national mastitis survey. The effect of herd and animal factors on somatic cell count

The purpose of the national mastitis survey was to collect information on the prevalence and causes of bovine subclinical mastitis in the Netherlands. Milk samples were collected once from 10 336 cows in a stratified random selection of herds (n = 227) in the Netherlands during the years 1985-1986. Results showed that 84.2 % of the cows were free from mastitis. Ten per cent of all udder quarters were infected, and 3.7 % of these were infected with Staphy(ococcus aureus, the main udder pathogen. Statistical analysis based on a 'split-plot' model was used to analyse the effect of herd factors and animal factors on somatic cell counts (SCC). Several factors significant­ly influenced SCC: breed, season, geographical region, type of housing, and the use of teat disin­fection. The effect of herd and animal factors on SCC of milk samples of individual cows was calculated as deviation from the geometric mean cell count of the standard cow (222 000/ml) and presented as the excpected SCC per cow. The interaction of parity x stage of lactation x infec­tion status also significantly influenced SCC. On the basis of expected SCC of uninfected cows correction factors were calculated for individual cows with various parities and at various stages of lactation. We conclude that the use of these correction factors can improve the analysis of SCC in the diagnosis of mastitis. <br/

A robust semi-automatic delineation workflow using denoised diffusion weighted magnetic resonance imaging for response assessment of patients with esophageal cancer treated with neoadjuvant chemoradiotherapy

Background and Purpose: Diffusion weighted magnetic resonance imaging (DW-MRI) can be prognostic for response to neoadjuvant chemotherapy (nCRT) in patients with esophageal cancer. However, manual tumor delineation is labor intensive and subjective. Furthermore, noise in DW-MRI images will propagate into the corresponding apparent diffusion coefficient (ADC) signal. In this study a workflow is investigated that combines a denoising algorithm with semi-automatic segmentation for quantifying ADC changes. Materials and Methods: Twenty patients with esophageal cancer who underwent nCRT before esophagectomy were included. One baseline and five weekly DW-MRI scans were acquired for every patient during nCRT. A self-supervised learning denoising algorithm, Patch2Self, was used to denoise the DWI-MRI images. A semi-automatic delineation workflow (SADW) was next developed and compared with a manually adjusted workflow (MAW). The agreement between workflows was determined using the Dice coefficients and Brand Altman plots. The prognostic value of ADCmean increases (%/week) for pathologic complete response (pCR) was assessed using c-statistics. Results: The median Dice coefficient between the SADW and MAW was 0.64 (interquartile range 0.20). For the MAW, the c-statistic for predicting pCR was 0.80 (95% confidence interval (CI):0.56–1.00). The SADW showed a c-statistic of 0.84 (95%CI:0.63–1.00) after denoising. No statistically significant differences in c-statistics were observed between the workflows or after applying denoising. Conclusions: The SADW resulted in non-inferior prognostic value for pCR compared to the more laborious MAW, allowing broad scale applications. The effect of denoising on the prognostic value for pCR needs to be investigated in larger cohorts.</p

A robust semi-automatic delineation workflow using denoised diffusion weighted magnetic resonance imaging for response assessment of patients with esophageal cancer treated with neoadjuvant chemoradiotherapy

Background and Purpose: Diffusion weighted magnetic resonance imaging (DW-MRI) can be prognostic for response to neoadjuvant chemotherapy (nCRT) in patients with esophageal cancer. However, manual tumor delineation is labor intensive and subjective. Furthermore, noise in DW-MRI images will propagate into the corresponding apparent diffusion coefficient (ADC) signal. In this study a workflow is investigated that combines a denoising algorithm with semi-automatic segmentation for quantifying ADC changes. Materials and Methods: Twenty patients with esophageal cancer who underwent nCRT before esophagectomy were included. One baseline and five weekly DW-MRI scans were acquired for every patient during nCRT. A self-supervised learning denoising algorithm, Patch2Self, was used to denoise the DWI-MRI images. A semi-automatic delineation workflow (SADW) was next developed and compared with a manually adjusted workflow (MAW). The agreement between workflows was determined using the Dice coefficients and Brand Altman plots. The prognostic value of ADCmean increases (%/week) for pathologic complete response (pCR) was assessed using c-statistics. Results: The median Dice coefficient between the SADW and MAW was 0.64 (interquartile range 0.20). For the MAW, the c-statistic for predicting pCR was 0.80 (95% confidence interval (CI):0.56–1.00). The SADW showed a c-statistic of 0.84 (95%CI:0.63–1.00) after denoising. No statistically significant differences in c-statistics were observed between the workflows or after applying denoising. Conclusions: The SADW resulted in non-inferior prognostic value for pCR compared to the more laborious MAW, allowing broad scale applications. The effect of denoising on the prognostic value for pCR needs to be investigated in larger cohorts.</p

Nova - Un nuage arc-en-ciel au-dessus des Alpes

Vidéo https://replay.jres.org/videos/watch/c0ce8c10-fc41-4cf7-9069-9d2c225f9e0aInternational audienc